import numpy as np
import pandas as pd
from itertools import cycle
from scipy import interp

# sklearn
from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing

# TensorFlow
import tensorflow as tf

# Timer
from timeit import default_timer as timer

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## progressbar
import progressbar

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
from matplotlib import cm
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Healthcare Analytics and Modeling

Heart Disease Dataset

• Exploratory Data Analysis
• Modeling: Tensorflow Linear Classifier
• Modeling: Tensorflow DNN Classifier
• Modeling: Tensorflow Boosted Trees Classifier
• Modeling: Tensorflow Boosted Trees Classifier with Feature Importance Analysis

Heart Disease Dataset

In this article, we demonstrate solving a classification problem in TensorFlow using Estimators using the Heart Disease Dataset from the UCI Machine Learning Repository.

Picture Source: harvard.edu

Attribute Information:

1. Age
2. Sex
   • 0: Female
   • 1: Male
3. Chest Pain Type
   • 1: Typical Angina
   • 2: Atypical Angina
   • 3: Non-Anginal Pain
   • 4: Asymptomatic

• Resting Blood Pressure

1. Serum Cholestoral (in mg/dl )
2. FBS: Fasting Blood Sugar > 120 mg/dl
   • 0 = False
   • 1 = True
3. Resting Electrocardiographic Results
   • 0: normal
   • 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
   • 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
4. Maximum Heart Rate Achieved
5. Exercise Induced Angina
   • 0: No
   • 1: Yes
6. Oldpeak = St Depression Induced By Exercise Relative To Rest
7. Slope: The Slope Of The Peak Exercise ST Segment
   • 1: Upsloping
   • 2: Flat
   • 3: Downsloping
8. Number Of Major Vessels (0-3) Colored By Flourosopy
9. Thal
   • 3: Normal
   • 6: Fixed Defect
   • 7: Reversable Defect

Variable to be predicted

• Heart Disease
  • 0: Absence
  • 1: Presence

Problem Description

Developing a predictive model that can predict whether heart disease is present or absent based on the rest of the given features.

Data = np.genfromtxt('heart-disease/heart.dat', delimiter=' ')
Attributes = ['Age', 'Sex', 'Chest Pain Type', 'Resting Blood Pressure', 'Serum Cholestoral',
              'Fasting Blood Sugar', 'Resting Electrocardiographic Results', 'Maximum Heart Rate Achieved',
              'Exercise Induced Angina', 'Oldpeak', 'Slope',
              'Number of Major Vessels', 'Thal', 'Heart Disease']
Data = pd.DataFrame(data = Data, columns = Attributes)
# 
Temp = ['Sex', 'Chest Pain Type', 'Fasting Blood Sugar', 'Resting Electrocardiographic Results',
       'Exercise Induced Angina', 'Slope', 'Number of Major Vessels','Thal']
for c in Temp:
    Data[c] = Data[c].astype(int).astype(str)
del Temp, c
Target = 'Heart Disease'
Labels_dict = dict(zip([0,1],['Absent', 'Present']))
Data['Heart Disease'] = (Data['Heart Disease']-1).astype(int)
#
display(Data.head(5))
display(pd.DataFrame({'Number of Instances': [Data.shape[0]], 'Number of Attributes': [Data.shape[1]]}).style.hide_index())

# Maps
Maps = {'Sex': {'0':'Female', '1':'Male'},
        'Chest Pain Type': {'1':'Typical Angina', '2':'Atypical Angina', '3': 'Non-Anginal Pain', '4':'Asymptomatic'},
        'Fasting Blood Sugar': {'0': 'False', '1': 'True'}, 'Exercise Induced Angina': {'0': 'No', '1': 'Yes'},
        'Slope': {'1': 'Upsloping', '2': 'Flat', '3': 'Downsloping'},
        'Thal':  {'3': 'Normal', '6': 'Fixed Defect','7': 'Reversable Defect'}}

	Age	Sex	Chest Pain Type	Resting Blood Pressure	Serum Cholestoral	Fasting Blood Sugar	Resting Electrocardiographic Results	Maximum Heart Rate Achieved	Exercise Induced Angina	Oldpeak	Slope	Number of Major Vessels	Thal	Heart Disease
0	70.0	1	4	130.0	322.0	0	2	109.0	0	2.4	2	3	3	1
1	67.0	0	3	115.0	564.0	0	2	160.0	0	1.6	2	0	7	0
2	57.0	1	2	124.0	261.0	0	0	141.0	0	0.3	1	0	7	1
3	64.0	1	4	128.0	263.0	0	0	105.0	1	0.2	2	1	7	0
4	74.0	0	2	120.0	269.0	0	2	121.0	1	0.2	1	1	3	0

Number of Instances	Number of Attributes
270	14

def Data_Plot(Inp, Title = None, W = None):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info = data_info.reset_index(drop = False).rename(columns = {'index':'Features'})
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type',
                 text = 'Percentage',
                 color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    if not W == None:
        fig.update_layout(width = W)
    fig.update_traces(texttemplate= 10*' ' + '%%{text}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    if not Title == None:
        fig.update_layout(title={'text': '<b>' + Title + '<b>', 'x':0.5,
                                         'y':0.90, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()
    return data_info

data_info = Data_Plot(Data, Title = 'Heart Disease Dataset', W = 800)

X and y sets

# A copy of the Dataframe
df = Data.copy()
# for TF
df.columns = [x.replace(' ','_') for x in df.columns] 
Temp = Target.replace(' ','_')
X = df.drop(columns = Temp)
y = df[Temp].values
del df, Temp

Training and testing sets

def DatasetTargetDist(Inp, Target, Labels_dict, PD):
    # Table
    Table = Inp[Target].value_counts().to_frame('Count').reset_index(drop = False).rename(columns = {'index':Target})
    Table[Target] = Table[Target].replace(Labels_dict)
    Table['Percentage'] = np.round(100*(Table['Count']/Table['Count'].sum()),2)
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=PD['column_widths'],
                        specs=[[{"type": "table"},{"type": "pie"}]])

    # Right
    fig.add_trace(go.Pie(labels=Table[Target].values, values=Table['Count'].values,
                         pull=PD['pull'], textfont=dict(size= PD['textfont']),
                         marker=dict(colors = PD['PieColors'], line=dict(color='black', width=1))), row=1, col=2)
    fig.update_traces(hole=PD['hole'])
    fig.update_layout(height = PD['height'], legend=dict(orientation="v"), legend_title_text= PD['legend_title'])
    
    # Left
    T = Table.copy()
    T['Percentage'] = T['Percentage'].map(lambda x: '%%%.2f' % x)
    Temp = []
    for i in T.columns:
        Temp.append(T.loc[:,i].values)

    fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
                                       fill_color= PD['TableColors'][0], align=['center','center'],
                                       font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
                           cells=dict(values=Temp, line_color='darkslategray',
                                      fill=dict(color= [PD['TableColors'][1], PD['TableColors'][1]]),
                                  align=['center', 'center'], font_size=12, height=20)), 1, 1)

    fig.update_layout(title={'text': '<b>' + Target + '<b>', 'x':PD['title_x'],
                                     'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()

Pull = [0 for x in range((len(Labels_dict)-1))]
Pull.append(.05)
PD = dict(PieColors = ['SeaGreen','FireBrick'],
          TableColors = ['Navy','White'], hole = .4,
          column_widths=[0.6, 0.4],textfont = 14, height = 350, tablecolumnwidth = [0.20, 0.12, 0.15],
          pull = Pull, legend_title = Target, title_x = 0.5, title_y = 0.8)
del Pull
DatasetTargetDist(Data, Target, Labels_dict, PD)

StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

Test_Size = 0.3
sss = StratifiedShuffleSplit(n_splits=1, test_size=Test_Size, random_state=42)
_ = sss.get_n_splits(X, y)
# For Tensorflow
X.columns = [x.replace(' ','_') for x in X.columns]

for train_index, test_index in sss.split(X, y):
    # X
    if isinstance(X, pd.DataFrame):
        X_train, X_test = X.loc[train_index], X.loc[test_index]
    else:
        X_train, X_test = X[train_index], X[test_index]
    # y    
    if isinstance(y, pd.Series):
        y_train, y_test = y[train_index], y[test_index]
    else:
        y_train, y_test = y[train_index], y[test_index]
del sss

def Train_Test_Dist(X_train, y_train, X_test, y_test, PD, Labels_dict = Labels_dict):
    def ToSeries(x):
        if not isinstance(x, pd.Series):
            Out = pd.Series(x)
        else:
            Out = x.copy()
        return Out
    
    fig = make_subplots(rows=1, cols=3, horizontal_spacing = 0.02, column_widths= PD['column_widths'],
                        specs=[[{"type": "table"},{'type':'domain'}, {'type':'domain'}]])
    # Right
    C = 2
    for y in [ToSeries(y_train).replace(Labels_dict), ToSeries(y_test).replace(Labels_dict)]:
        fig.add_trace(go.Pie(labels= list(Labels_dict.values()),
                             values= y.value_counts().values, pull=PD['pull'],
                             textfont=dict(size=PD['textfont']),
                             marker=dict(colors = PD['PieColors'],
                             line=dict(color='black', width=1))), row=1, col=C)
        fig.update_traces(hole=.5)
        fig.update_layout(legend=dict(orientation="v"), legend_title_text= PD['legend_title'])
        C+=1
    
    # Left
    
    # Table
    Table = pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
                       'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).astype(str)
    T = Table.copy()
    Temp = []
    for i in T.columns:
        Temp.append(T.loc[:,i].values)
        
    TableColors = PD['TableColors']
    fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
                                       fill_color= TableColors[0], align=['center','center'],
                                       font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
                           cells=dict(values=Temp, line_color='darkslategray',
                                      fill=dict(color= [TableColors[1], TableColors[1]]),
                                  align=['center', 'center'], font_size=12, height=20)), 1, 1)
    fig.update_layout(title={'text': '<b>' + 'Dataset Distribution' + '<b>', 'x':PD['title_x'],
                                     'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
    if not PD['height'] == None:
        fig.update_layout(height = PD['height'])
    fig.show()
    
PD.update(dict(column_widths=[0.3, 0.3, 0.3], tablecolumnwidth = [0.2, 0.4], height = 350, legend_title = Target))

Train_Test_Dist(X_train, y_train, X_test, y_test, PD)

Modeling: Tensorflow Boosted Trees Classifier with Feature Importance Analysis

Feature Columns

Create the feature columns, using the original numeric columns as is and one-hot-encoding categorical variables.

def Feat_Columns(Inp, Numeric = False, disp_dtype = False):
    '''
    Feature Columns function
    Input: Dataset
    Output: Tensorflow Feature Column List
    '''
    if not Numeric:
        Numeric = ['int64', 'int32', 'float64', 'float32']
    Temp = Inp.dtypes.reset_index(drop = False)
    Temp.columns = ['Features', 'Data Type']
    Temp['Data Type'] = Temp['Data Type'].astype(str)
    # Numeric_Columns
    Numeric_Columns = Temp.loc[Temp['Data Type'].isin(Numeric), 'Features'].tolist()
    # Categorical_Columns
#     Categorical_Columns = Temp.loc[(~Temp['Data Type'].isin(Numeric)), 'Features'].tolist()
    Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
    if disp_dtype:
        display(pd.DataFrame({'Numeric Columns': [', '.join(Numeric_Columns)],
                  'Categorical Columns': [', '.join(Categorical_Columns)]}, index = ['Columns']).T.style)
    # Feature Columns
    feature_columns = []
    if len(Categorical_Columns)>0:
        for feature_name in Categorical_Columns:
            vocabulary = Inp[feature_name].unique()
            feature_columns.append(tf.feature_column.indicator_column(\
                                      tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)))
    if len(Numeric_Columns)>0:
        for feature_name in Numeric_Columns:
            feature_columns.append(tf.feature_column.numeric_column(feature_name))
    return feature_columns

Input Function

The input function specifies how data is converted to a tf.data.Dataset that feeds the input pipeline in a streaming fashion. Moreover, an input function is a function that returns a tf.data.Dataset object which outputs the following two-element tuple:

• features - A Python dictionary in which:
  • Each key is the name of a feature.
  • Each value is an array containing all of that feature's values.
• label - An array containing the values of the label for every example.

def make_input_fn(X, y, inmemory_train = False, n_epochs= None, shuffle=True, batch_size = 256):
    # Not In memory Training
    if not inmemory_train:
        def input_fn():
            dataset = tf.data.Dataset.from_tensor_slices((X.to_dict(orient='list'), y))
            if shuffle:
                dataset = dataset.shuffle(1000)
            dataset = (dataset.repeat(n_epochs).batch(batch_size))
            return dataset
    # In memory Training
    if inmemory_train:
        y = np.expand_dims(y, axis=1)
        def input_fn():
            return dict(X), y
    # End
    return input_fn

Building the input pipeline

my_feature_columns = Feat_Columns(X)
# Training and evaluation input functions.
train_input_fn = make_input_fn(X_train, y_train)
eval_input_fn = make_input_fn(X_test, y_test, shuffle=False, n_epochs=1)

Training the model

# Classifier
tf.keras.backend.clear_session()
IT = int(1e3)
params = {'n_trees': 50, 'max_depth': 3, 'n_batches_per_layer': 1, 'center_bias': True}
classifier =  tf.estimator.BoostedTreesClassifier(my_feature_columns, **params)
# Train model.
start = timer()
classifier.train(train_input_fn, max_steps = IT)
CPU_Time = timer() - start
# Evaluation.
results = classifier.evaluate(eval_input_fn)
clear_output()
results['CPU Time'] = CPU_Time
display(pd.DataFrame(results, index = ['']).round(4))

	accuracy	accuracy_baseline	auc	auc_precision_recall	average_loss	label/mean	loss	precision	prediction/mean	recall	global_step	CPU Time
	0.8642	0.5556	0.9093	0.8667	0.3973	0.4444	0.3973	0.8205	0.4867	0.8889	178	5.3084

ROC Curves

def ROC_Curve(y_test, probs, n_classes, FS = 7, ax = False, pad = 0.01):
    # converting y_test to categorical
    y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes=n_classes, dtype='float32')
    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(n_classes):
        fpr[i], tpr[i], _ = metrics.roc_curve(y_test_cat[:, i], probs[:, i])
        roc_auc[i] = metrics.auc(fpr[i], tpr[i])

    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = metrics.roc_curve(y_test_cat.ravel(), probs.ravel())
    roc_auc["micro"] = metrics.auc(fpr["micro"], tpr["micro"])

    # First aggregate all false positive rates
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

    # Then interpolate all ROC curves at this points
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(n_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])

    # Finally average it and compute AUC
    mean_tpr /= n_classes

    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = metrics.auc(fpr["macro"], tpr["macro"])

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=[0, 1], y=[0, 1], name = 'FPR = TPR', line = dict(color='Black', width=2, dash='dash')))
    fig.add_trace(go.Scatter(x=fpr["micro"], y=tpr["micro"], mode='lines', marker_color = 'deeppink',
                             name='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"])))
    fig.add_trace(go.Scatter(x=fpr["macro"], y=tpr["macro"], mode='lines', marker_color = 'navy',
                             name='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"])))

    colors = cycle(['Aqua', 'DarkOrange', 'CornflowerBlue'])
    for i, color in zip(range(n_classes), colors):
        _ = fig.add_trace(go.Scatter(x = fpr[i], y = tpr[i], mode='lines', marker_color= px.colors.sequential.Rainbow,
                                     name='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i])))

    # Background
    fig.update_layout(plot_bgcolor= 'white')
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range =[-pad, 1+pad],
                     title = 'False Positive Rate (FPR)')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range =[-pad, 1+pad],
                     title = 'True Positive Rate (TPR)')
    fig.update_yaxes(scaleanchor = "x", scaleratio = 1)
    fig.update_layout(height = 600, width = 810)
    fig.update_layout(title={'text': '<b>' + 'Receiver Operating Characteristic (ROC) Curves' + '<b>', 'x': .5,
                                         'y': .9, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()

pred_dicts = list(classifier.predict(eval_input_fn))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])    
ROC_Curve(y_test, probs, n_classes = len(Labels_dict), FS = 8)

Feature Importance

We can investigate the feature importance of an artificial classification task. This is similar to that of scikit-learn and has been outlined in [6].

pred_dicts = list(classifier.experimental_predict_with_explanations(eval_input_fn))
clear_output()

# Create DFC Pandas dataframe.
labels = y_test
probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])
df_dfc = pd.DataFrame([pred['dfc'] for pred in pred_dicts])
df_dfc.columns = [x.replace('_',' ') for x in df_dfc.columns]
display(df_dfc.describe().T.style.background_gradient(subset= ['mean'], cmap='RdYlGn')\
        .background_gradient(subset= ['std'], cmap='RdYlGn')\
        .background_gradient(subset= ['min'], cmap='hot')\
        .background_gradient(subset= ['max'], cmap='winter')
        .set_precision(4).format({'count': "{:.0f}"}))

	count	mean	std	min	25%	50%	75%	max
Number of Major Vessels	81	0.0154	0.1060	-0.1366	-0.0683	-0.0356	0.1020	0.2979
Chest Pain Type	81	0.0037	0.1053	-0.2074	-0.0824	0.0446	0.0867	0.1882
Thal	81	0.0090	0.0880	-0.1044	-0.0680	-0.0488	0.0877	0.1807
Slope	81	0.0008	0.0719	-0.1815	-0.0582	-0.0225	0.0602	0.1473
Resting Blood Pressure	81	0.0039	0.0628	-0.1854	-0.0341	-0.0003	0.0284	0.2500
Sex	81	0.0186	0.0685	-0.1188	-0.0341	0.0286	0.0497	0.1455
Maximum Heart Rate Achieved	81	0.0033	0.0846	-0.1506	-0.0661	0.0058	0.0760	0.1988
Exercise Induced Angina	81	0.0030	0.0264	-0.0766	-0.0074	-0.0042	0.0200	0.0713
Oldpeak	81	-0.0059	0.0721	-0.1866	-0.0450	-0.0201	0.0229	0.2642
Serum Cholestoral	81	0.0017	0.0618	-0.1649	-0.0354	0.0041	0.0292	0.2112
Fasting Blood Sugar	81	-0.0004	0.0101	-0.0454	0.0000	0.0004	0.0012	0.0301
Resting Electrocardiographic Results	81	0.0001	0.0114	-0.0322	-0.0008	0.0000	0.0004	0.0515
Age	81	-0.0040	0.0607	-0.1818	-0.0357	-0.0083	0.0326	0.2105

A nice property of DFCs is that the sum of the contributions + the bias is equal to the prediction for a given example.

# Sum of DFCs + bias == probabality.
bias = pred_dicts[0]['bias']
dfc_prob = df_dfc.sum(axis=1) + bias
np.testing.assert_almost_equal(dfc_prob.values, probs.values)

Feature Importance for a patient

Plot DFCs for an individual patient which is color-coded based on the contributions' directionality and add the feature values on the figure.

def _add_feature_values(feature_values, ax, colors):
    """Display feature's values on left of plot."""
    x_coord = ax.get_xlim()[0]
    OFFSET = 0.15
    for y_coord, (feat_name, feat_val) in enumerate(feature_values.items()):
        t = plt.text(x_coord, y_coord - OFFSET, '{}'.format(feat_val), size=12)
        t.set_bbox(dict(facecolor= colors[y_coord], alpha=0.25))
    font = FontProperties()
#     font.set_weight('bold')
    t = plt.text(x_coord, y_coord + 1 - OFFSET, 'Feature\nValue', fontproperties=font, size=13)

def _yaxis_labels(ax):
    y_labels = []
    for c in [c.get_text() for c in ax.get_yticklabels()]:
        List = list(mit.locate(c, lambda x: x == " "))
        if len(List)>1:
            List = List[1::2]
            Temp1 = list(c)
            for position in List:
                Temp1[position] = '\n'
            c = "".join(Temp1)
        y_labels.append(c)
    return y_labels

def _xLims(ax):
    Temp = np.linspace(-1,1,21, endpoint=True)
    Temp = np.round(Temp,1)
    xlims = ax.get_xlim()
    for l, r in list(zip(Temp[:-1],Temp[1:])):
        if l<= xlims[0] < r:
            Left = l
        if l<= xlims[1] < r:
            Right = r
    return [Left, Right]

def Plot_Example(example, TOP_N = 10, Pos_Color = 'LimeGreen', Neg_Color = 'OrangeRed', Maps = Maps, FS = (13, 7)):
    example.index = [x.replace('_',' ') for x in example.index]
    # Sorting by absolute value
    sorted_ix = example.abs().sort_values()[-TOP_N:].index
    example = example[sorted_ix]
    
    fig, ax = plt.subplots(1, 1, figsize= FS)
    Temp = example.to_frame('Value').sort_index(ascending= False)
    Temp0 = Temp.copy(); Temp0[Temp0 < 0] = np.nan
    _ = Temp0.plot(kind='barh', color= Pos_Color, edgecolor = 'white', hatch = '///', legend=None, alpha=0.75, ax = ax)
    Temp0 = Temp.copy(); Temp0[Temp0 >= 0] = np.nan
    _ = Temp0.plot(kind='barh', color= Neg_Color, edgecolor = 'white', hatch = '///', legend=None, alpha=0.75, ax = ax)
    _ = Temp.plot(kind='barh', color='None', edgecolor = 'Black', legend=None, alpha=1, lw=1.2, ax = ax)
    del Temp, Temp0
    _ = ax.grid(False, axis='y')
    # Y axis Labels

#     _ = ax.set_yticklabels(_yaxis_labels(ax), size=12)
    # x axis Limits
    _ = ax.set_xlim(_xLims(ax))
    
    # Add feature values.
    Temp = X_test.copy()
    Temp.columns = [x.replace('_',' ') for x in Temp.columns]
    for c in Maps.keys():
        Temp[c] = Temp[c].map(Maps[c])
  
    colors = example.map(lambda x: Pos_Color if x >= 0 else Neg_Color).tolist()
    _add_feature_values(Temp.iloc[ID][sorted_ix], ax, colors)
    return ax

# Plot results.
ID = 20
Tops = X_train.shape[1]
ax = Plot_Example(df_dfc.iloc[ID], TOP_N = Tops, FS = (13, 8))
_ = ax.set_title('Feature contributions for example patient {} from the Test set\n Pred: {:1.2f}; Label: {}'
                 .format(ID, probs[ID], labels[ID]))
_ = ax.set_xlabel('Contribution to Predicted Probability', size=14)

Global feature importances

Gain-based feature importances

Gain-based feature importances are built into the TensorFlow Boosted Trees estimators using classifier.experimental_feature_importances.

importances = classifier.experimental_feature_importances(normalize=True)
df_imp = pd.Series(importances)
Temp = df_imp.iloc[0:Tops][::-1].reset_index()
Temp.columns = ['Features','Importance']
Temp['Features'] = Temp['Features'].map(lambda x: x.replace('_',' '))

importances = classifier.experimental_feature_importances(normalize=True)

def Plot_FeatImportance(pds, TOP_N = 10, FS = (13, 6)):
    Temp = pds.iloc[0:TOP_N][::-1].reset_index()
    Temp.columns = ['Features','Importance']
    Temp = Temp.sort_values(by=['Importance'])
    Temp['Features'] = Temp['Features'].map(lambda x: x.replace('_',' '))
    
    fig, ax = plt.subplots(1, 1, figsize = FS)
    CP = sns.color_palette("RdYlGn", TOP_N)
    _ = sns.barplot(ax = ax, x='Importance', y= 'Features', data= Temp, palette=CP, hatch = '//')
    _ = sns.barplot(ax = ax, x='Importance', y= 'Features', data= Temp, facecolor = 'None', edgecolor = 'Indigo')
    _ = ax.grid(False, axis='y')
    # Y axis Labels
#     _ = ax.set_yticklabels(_yaxis_labels(ax), size=12)
    # x axis Limits
    _ = ax.set_xlim(_xLims(ax))
    return ax
    
ax = Plot_FeatImportance(pd.Series(importances), TOP_N = Tops, FS = (13, 7.5))
_ = ax.set_xlim(right = .16)

Average absolute DFCs

ax = Plot_FeatImportance(df_dfc.abs().mean(), TOP_N = Tops, FS = (13, 7.5))
_ = ax.set_xlim(right = .1)

Permutation feature importance

def permutation_importances(est, X_eval, y_test, metric, features):
    """Column by column, shuffle values and observe effect on eval set.

    source: http://explained.ai/rf-importance/index.html
    A similar approach can be done during training. See "Drop-column importance"
    in the above article."""
    baseline = metric(est, X_eval, y_test)
    imp = []
    for col in features:
        save = X_eval[col].copy()
        X_eval[col] = np.random.permutation(X_eval[col])
        m = metric(est, X_eval, y_test)
        X_eval[col] = save
        imp.append(baseline - m)
    return np.array(imp)

def accuracy_metric(est, X, y):
    """TensorFlow estimator accuracy."""
    eval_input_fn = make_input_fn(X,
                                  y=y,
                                  shuffle=False,
                                  n_epochs=1)
    return est.evaluate(input_fn=eval_input_fn)['accuracy']
features = X_train.columns.tolist()
importances = permutation_importances(classifier, X_test, y_test, accuracy_metric,
                                      features)
df_imp = pd.Series(importances, index=features)
clear_output()

ax = Plot_FeatImportance(df_imp, TOP_N = Tops, FS = (13, 7.5))

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References

1. Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J.J., Sandhu, S., Guppy, K.H., Lee, S. and Froelicher, V., 1989. International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology, 64(5), pp.304-310.
2. Aha, D. and Kibler, D., 1988. Instance-based prediction of heart-disease presence with the Cleveland database. University of California, 3(1), pp.3-2.
3. Gennari, J.H., Langley, P. and Fisher, D., 1989. Models of incremental concept formation. Artificial intelligence, 40(1-3), pp.11-61.
4. Regression analysis Wikipedia page
5. Tensorflow tutorials
6. TensorFlow Boosted Trees Classifier
7. Lasso (statistics) Wikipedia page)
8. Tikhonov regularizationm Wikipedia page
9. Palczewska A., Palczewski J., Marchese Robinson R., Neagu D. (2014) Interpreting Random Forest Classification Models Using a Feature Contribution Method. In: Bouabana-Tebibel T., Rubin S. (eds) Integration of Reusable Systems. Advances in Intelligent Systems and Computing, vol 263. Springer, Cham
10. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.
11. Jordi Warmenhoven, ISLR-python
12. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). ISLR: Data for an Introduction to Statistical Learning with Applications in R

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